A new terminal agent model named Tmax-27B has been released, built upon Qwen3.6-27B and trained using DPPO for reinforcement learning. This model achieves competitive scores on agentic benchmarks like Terminal Bench 2.0. To make Tmax-27B accessible on consumer hardware, a variety of quantized GGUF versions have been created, ranging from 2 to 5 bits per weight, incorporating a speculative decoding head for improved performance. AI
IMPACT Provides a more accessible version of a capable terminal agent for researchers and developers with limited hardware.
RANK_REASON Release of a new model with performance benchmarks and quantization details for accessibility. [lever_c_demoted from research: ic=1 ai=1.0]
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